Methods and apparatus for the application of machine learning to radiographic images of animals

ABSTRACT

Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines. Computer-readable apparatus and computing systems are also disclosed.

PRIORITY

This application is a divisional of, and claims the benefit of priorityto, co-owned and co-pending U.S. patent application Ser. No. 16/578,182filed Sep. 20, 2019 and entitled “Methods and Apparatus for theApplication of Machine Learning to Radiographic Images of Animals”,which claims the benefit of priority to U.S. Provisional PatentApplication Ser. No. 62/808,604 filed Feb. 21, 2019 and entitled“Methods and Apparatus for the Application of Machine Learning toRadiographic Images of Animals”, the contents of each of the foregoingbeing incorporated herein by reference in its entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialwhich is subject to copyright or mask work) protection. The (copyrightor mask work) owner has no objection to the facsimile reproduction byanyone of the patent document or the patent disclosure, as it appears inthe Patent and Trademark Office patent file or records, but otherwisereserves all copyright or mask work) rights whatsoever.

BACKGROUND OF THE DISCLOSURE 1. Technological Field

The present disclosure relates generally to the application of machinelearning to biological data received from living beings, and moreparticularly in one exemplary aspect to the application of machinelearning to radiographic images of various animal species including,inter alia, canines, felines, other domesticated and non-domesticatedanimals, and humans.

2. Field of the Disclosure

The utilization of radiology for the capture of radiological images ofvarious species is a mature technology that is widely deployed inmedical centers throughout the world. For example, the use of variousmedical imaging techniques enables a veterinarian to diagnose and treata wide variety of maladies thereby improving the animal's quality oflife. Despite the numerous benefits associated with radiology generally,numerous deficiencies associated with veterinary radiology exist. Forexample, the capture of radiological images is a significant stressorfor the animal, which can result in the capture of poor-qualityradiological images due to, for example, animal movement during imagecapture. Compounding this deficiency is the need to recaptureradiological images when the original capture was of poor-quality (e.g.,due to radiology technician error), thereby adding additional stress tothe animal. Moreover, qualified radiologists are a limited resourceresulting in significant delays between the time of image capture andthe subsequent reading of these captured images. These delays can resultin additional damage, discomfort and/or death for the animal seekingveterinary treatment. Accordingly, improved methods and apparatus areneeded to address these, and other known deficiencies present in theprior art.

SUMMARY

The present disclosure satisfies the foregoing needs by providing, interalia, methods and apparatus for the application of machine learning toradiological images of animals.

In one aspect, a method of training a plurality of classificationartificial intelligence engines for classification of various maladiesof an animal is disclosed. In one embodiment, the method includesreceiving a set of radiographic images captured of the animal; applyingone or more transformations to at least a portion of the set ofradiographic images captured of the animal, the applying including oneor more of rotating, translating, and normalizing to create a modifiedset of radiographic images; segmenting the modified set of radiographicimages using one or more segmentation artificial intelligence engines tocreate a set of segmented radiographic images; feeding the set ofsegmented radiographic images to respective ones of the plurality ofclassification artificial intelligence engines; outputting results fromthe plurality of classification artificial intelligence engines for theset of segmented radiographic images to an output decision engine; andadding the set of segmented radiographic images and the output resultsfrom the plurality of classification artificial intelligence engines toa training set for one or more of the plurality of classificationartificial intelligence engines.

In one variant, subsequent to the applying and prior to the segmenting,the method further includes determining whether any anatomy for theanimal has been missed within the modified set of radiographic imagesusing an image quality engine; determining whether any image twisting ispresent within the modified set of radiographic images using the imagequality engine; determining whether burn through has been detectedwithin the modified set of radiographic images using the image qualityengine; and when any of the acts of determining by the image qualityengine has identified an issue, transmitting a notification of theidentified issue to a person responsible for capture of the set ofradiographic images.

In another variant, the method further includes applying a training setassistance procedure to the output results, the applying of the trainingset assistance procedure includes: verifying the output results by aquality control group; subsequent to the verifying, forwarding on theoutput results to a requesting doctor of veterinary medicine (DVM);receiving clinician verification from the requesting DVM; verifying theclinician verification from the requesting DVM; and if necessary,updating the training set for the one or more of the plurality ofclassification artificial intelligence engines.

In yet another variant, the method further includes determining that theoutput results exceed a threshold value for one of the plurality ofclassification artificial intelligence engines; and removing thetraining set assistance procedure for the one of the plurality ofclassification artificial intelligence engines.

In yet another variant, the method further includes determining that theoutput results do not exceed a threshold value for a second of theplurality of classification artificial intelligence engines; and keepingthe training set assistance procedure for the second of the plurality ofclassification artificial intelligence engines.

In yet another variant, the updating of the training set for the one ormore of the plurality of classification artificial intelligence enginesfurther includes removing the set of segmented radiographic images andthe output results from the training set for the one or more of theplurality of classification artificial intelligence engines.

In another aspect, a non-transitory computer-readable storage apparatusis disclosed. In one embodiment, the non-transitory computer-readablestorage apparatus includes a plurality of instructions, that whenexecuted by a processor apparatus, are configured to: receive a set ofradiographic images captured of an animal; apply one or moretransformations to at least a portion of the set of radiographic imagescaptured of the animal, the application including one or more of arotation operation, a translation operation, and a normalizationoperation to create a modified set of radiographic images; segment themodified set of radiographic images using one or more segmentationartificial intelligence engines to create a set of segmentedradiographic images; feed the set of segmented radiographic images torespective ones of the plurality of classification artificialintelligence engines; output results from the plurality ofclassification artificial intelligence engines for the set of segmentedradiographic images to an output decision engine; and add the set ofsegmented radiographic images and the output results from the pluralityof classification artificial intelligence engines to a training set forone or more of the plurality of classification artificial intelligenceengines.

In one variant, the plurality of instructions, when executed by theprocessor apparatus, are further configured to output results from theoutput decision engine to a graphical user interface (GUI), the GUIincluding the modified set of radiographic images, a centralizedradiographic image from the modified set of radiographic images, and aplurality of classifications.

In another variant, the plurality of instructions, when executed by theprocessor apparatus, are further configured to receive a first selectionfor one of the plurality of classifications; and highlight one or moreof the modified set of radiographic images that were utilized inassessing the first selected one of the plurality of classifications.

In yet another variant, the plurality of instructions, when executed bythe processor apparatus, are further configured to cause display of oneof the highlighted one or more of the modified set of radiographicimages as the centralized radiographic image.

In yet another variant, the plurality of instructions, when executed bythe processor apparatus, are further configured to cause display of afirst segmentation outline within the centralized radiographic image,the first segmentation outline representing a first anatomical area ofinterest utilized in the assessment of the first selected one of theplurality of classifications.

In yet another variant, the plurality of instructions, when executed bythe processor apparatus, are further configured to receive a secondselection for one of the plurality of classifications, the secondselection differing from the first selection; and highlight one or moreof the modified set of radiographic images that were utilized inassessing the second selected one of the plurality of classifications.

In yet another variant, the plurality of instructions, when executed bythe processor apparatus, are further configured to cause display of asecond segmentation outline within the centralized radiographic image,the second segmentation outline differing from the first segmentationoutline, the second segmentation outline representing a secondanatomical area of interest utilized in the assessment of the secondselected one of the plurality of classifications.

In yet another aspect, a system for training a plurality ofclassification artificial intelligence engines for classification ofvarious maladies of an animal is disclosed. In one embodiment, thesystem includes an image quality engine which receives as input,biological data as well as one or more quality control parameters; oneor more segmentation artificial intelligence engines that are trainedusing one or more segmentation training sets; one or more classificationartificial intelligence engines that are trained using one or moreclassification training sets; and an output decision engine thatreceives as input, one or more outputs from the one or moreclassification artificial intelligence engines.

In one variant, the system is configured to receive a set ofradiographic images captured of the animal by the image quality engine;apply, by the image quality engine, one or more transformations to atleast a portion of the set of radiographic images captured of theanimal, the application including one or more of a rotation operation, atranslation operation, and a normalization operation to create amodified set of radiographic images; segment the modified set ofradiographic images using the one or more segmentation artificialintelligence engines to create a set of segmented radiographic images;feed the set of segmented radiographic images by the one or moresegmentation artificial intelligence engines to respective ones of theone or more classification artificial intelligence engines; outputresults from the one or more classification artificial intelligenceengines for the set of segmented radiographic images to the outputdecision engine; and add the set of segmented radiographic images andthe output results from the one or more classification artificialintelligence engines to the one or more classification training sets.

In another variant, the image quality engine is further configured todetermine whether any anatomy for the animal has been missed within themodified set of radiographic images; determine whether any imagetwisting is present within the modified set of radiographic images;determine whether burn through has been detected within the modified setof radiographic images; and when the image quality engine has identifiedan issue, the image quality engine is configured to transmit anotification of the identified issue to a person responsible for captureof the set of radiographic images.

In yet another variant, the image quality engine is located proximate toa location where the set of radiographic images have been captured ofthe animal, the image quality engine being located remote from the oneor more classification artificial intelligence engines.

In yet another variant, the image quality engine is configured to notonly identify the issue, but facilitate correction of the identifiedissue.

In yet another variant, the system is further configured to apply atraining set assistance procedure to the output results, the applicationof the training set assistance procedure including verification of theoutput results by a quality control group; subsequent to theverification, forward on the output results to a requesting doctor ofveterinary medicine (DVM); receive clinician verification from therequesting DVM; verify the clinician verification from the requestingDVM; and if necessary, update the training set for the one or moreclassification training sets.

In yet another variant, the system is further configured to determinethat the output results exceed a threshold value for one of the one ormore classification artificial intelligence engines; and remove theapplication training set assistance procedure for the one of the one ormore classification artificial intelligence engines.

Other features and advantages of the present disclosure will immediatelybe recognized by persons of ordinary skill in the art with reference tothe attached drawings and detailed description of exemplaryimplementations as given below.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, objectives, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings, wherein:

FIG. 1 is a logical block diagram of one exemplary system forclassifying conditions of an animal based on biological data, inaccordance with the principles of the present disclosure.

FIG. 2A is a logical flow diagram of one exemplary method for theutilization of a image quality engine, in accordance with the principlesof the present disclosure.

FIG. 2B is a logical flow diagram of one exemplary method for thetraining of a classification artificial intelligence engine, inaccordance with the principles of the present disclosure.

FIG. 3A is a first exemplary graphical user interface display indicativeof classifications and confidence levels for a plurality of animals, inaccordance with the principles of the present disclosure.

FIG. 3B is a second exemplary graphical user interface displayindicative of a classification for a given animal, in accordance withthe principles of the present disclosure.

FIG. 3C is a third exemplary graphical user interface display indicativeof a classification for a given animal, in accordance with theprinciples of the present disclosure.

FIG. 3D is a fourth exemplary graphical user interface displayindicative of a classification for a given animal, in accordance withthe principles of the present disclosure.

FIG. 3E is a fifth exemplary graphical user interface display indicativeof a classification for a given animal, in accordance with theprinciples of the present disclosure.

FIG. 3F is a sixth exemplary graphical user interface display indicativeof the types of classifications for a given image, in accordance withthe principles of the present disclosure.

FIG. 3G is a seventh exemplary graphical user interface indicative of asummary of classifications for a given animal, in accordance with theprinciples of the present disclosure.

-   -   All Figures disclosed herein are © Copyright 2019 Westside        Veterinary Innovation, LLC. All rights reserved.

DETAILED DESCRIPTION

Overview

Upon establishment (or identification) of basic anatomical criteria,embodiments of the present disclosure contained herein will “learn”about, for example, the radiographic data contained within, for example,captured radiographic images using a database of previously capturedradiographic images. This database of captured radiographic images mayinclude, for example, tens of thousands of “smart” labeled imagesreviewed by qualified expert veterinarian radiologists underquality-controlled processes. These images located in the database maybe then used to train machine learning algorithms, which are in turnused on newly received images in order to assist in classification anddetection of various maladies. These newly received images may in turnbe added to this database of radiographic images in order to furtherimprove the machine learning algorithms. In this fashion, the machinelearning algorithms may be allowed to continuously adapt through use(and training) of these machine learning algorithms in order to improveupon its classifications. Through the application of machine learningalgorithms to the captured radiographic images, the machine learningalgorithms may aid in the identification and classification of varioushealth issue(s) that may be associated with the animal.

In some implementations, the use of the machine learning algorithmsinvolves use of both: (a) a segmentation methodology using one or moresegmentation artificial engine(s); and (b) a classification methodologyusing one or more classification artificial intelligence engine(s). Thesegmentation methodology may identify specific anatomical area(s) of theanimal (e.g., organs, bones, etc.) thereby assisting with theclassification, while the classification methodology may classify one ormore maladies and/or other identified issues such as, for example, bonefractures, illnesses, disorders, infections, and other common ailments.After execution of the segmentation methodology, the software willexecute the classification methodology on these segmented images. Theclassification methodology may subsequently “grade” the severity of theidentified issue using a grading scale that may vary between medicallynormal to medically abnormal.

Upon execution of the segmentation methodology and/or the classificationmethodology, the computing system responsible for the classification maytransmit a response back to the requesting doctor of veterinarianmedicine (DVM). The response may include the graded severity (e.g.,strong, medium, or weak, etc.) and may also “highlight” the identifiedregions within the radiographic images used for the classification withadditional margin to ensure the region of interest can be viewedclearly. The response may also include recommended courses of actionincluding, for example, further diagnostics that may be needed in orderto support the DVM and the animal and may also include treatmentrecommendations for the animal that may be based upon, for example,historical success rates. Embodiments disclosed herein create theability to transfer the radiographic images to clinical applicabilityand may also be readily applied across a wide swathe of different sizesand anatomies for the animals being examined.

The present disclosure also describes an image quality engine that maybe governed by various quality control parameters and/or machinelearning algorithms. One disclosed benefit of this image quality engineis to ensure the quality of the radiographic images taken as well as tominimize the stress to the animal while these radiographic images arecaptured. For example, the image quality engine may also be implementedon-site at the location of the DVM's office, thereby enabling the imagequality engine to provide near-real-time feedback to, for example, thetechnicians responsible for capturing the radiographic images.Additionally, the use of the segmentation and classificationmethodologies enables animals with more serious maladies to be treatedbefore those animals with less (or no) serious maladies.

EXEMPLARY EMBODIMENTS

Detailed descriptions of the various embodiments and variants of theapparatus and methods of the present disclosure are now provided. It isnoted that wherever practicable similar or like reference numbers may beused in the figures and may indicate similar or like functionality. Thefigures depict embodiments of the disclosed system (or methods) forpurposes of illustration only. One skilled in the art will readilyrecognize from the following description that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutnecessarily departing from the principles described herein.

While primarily discussed in the context of the application of machinelearning to radiographic images such as, for example, two-dimensional(planar) radiological images (e.g., X-rays), it is not necessarily aprerequisite that the application of machine learning to images beconfined to two-dimensional images. For example, it is appreciated thatvariants of the present disclosure may be readily applied to othertwo-dimensional and three-dimensional imaging techniques such ascomputed tomography (CT), magnetic resonance imaging (MRI), positronemission tomography (PET), single-photon emission computed tomography(SPECT), ultrasound (sonography) and/or other biological imagingtechniques. These and other variants would be readily apparent to one ofordinary skill given the contents of the present disclosure.

Moreover, while primarily discussed in the context of the application ofmachine learning to biological imaging data, the present disclosure hasbroader usefulness outside of biological imaging. For example, thetechniques described herein may be applied to other biometric data (asdiscussed subsequently herein) including, for example, vital signs suchas pulse, temperature, respiratory rate and the like in order to assistin the identification and treatment of various physiological conditionsof an animal. Moreover, other biometric data may be utilized as well,such as audio signals obtained via, for example, a stethoscope (e.g.,auscultation of heart sound) and/or use of blood, fecal, and/or otherbodily fluid test results. These other biological indicators may be usedby the classification and output decision engine(s) in addition to, oralternatively from, the biological imaging techniques described hereinto give a more holistic view of the health of an animal.

Exemplary Classification System(s)—

Referring now to FIG. 1, one exemplary system 100 for classifyingconditions of an animal based on biological data are shown and describedin detail. The functionality of the various modules described herein maybe implemented through the use of software executed by one or moreprocessors (or controllers) and/or may be executed via the use of one ormore dedicated hardware modules, with the architecture of the systembeing specifically optimized to execute the artificial intelligenceand/or machine learning architectures discussed herein. The computercode (software) disclosed herein is intended to be executed by acomputing system that is able to read instructions from a non-transitorycomputer-readable medium and execute them in one or more processors (orcontrollers), whether off-the-shelf or custom manufactured. Thecomputing system may be used to execute instructions (e.g., program codeor software) for causing the computing system to execute the computercode described herein. In some implementations, the computing systemoperates as a standalone device or a connected (e.g., networked) devicethat connects to other computer systems. The computing system mayinclude, for example, a personal computer (PC), a tablet PC, a notebookcomputer, or other custom device capable of executing instructions(sequential or otherwise) that specify actions to be taken. In someimplementations, the computing system may include a server. In anetworked deployment, the computing system may operate in the capacityof a server or client in a server-client network environment, or as apeer device in a peer-to-peer (or distributed) network environment.Moreover, a plurality of computing systems may operate to jointlyexecute instructions to perform any one or more of the methodologiesdiscussed herein.

An exemplary computing system includes one or more processing units(generally processor apparatus). The processor apparatus may include,for example, a central processing unit (CPU), a graphics processing unit(GPU), a digital signal processor (DSP), a controller, a state machine,one or more application specific integrated circuits (ASICs), one ormore radio-frequency integrated circuits (RFICs), or any combination ofthe foregoing. The computing system also includes a main memory. Thecomputing system may include a storage unit. The processor, memory andthe storage unit may communicate via a bus.

In addition, the computing system may include a static memory, a displaydriver (e.g., to drive a plasma display panel (PDP), a liquid crystaldisplay (LCD), a projector, or other types of displays). The computingsystem may also include input/output devices, e.g., an alphanumericinput device (e.g., touch screen-based keypad or an external inputdevice such as a keyboard), a dimensional (e.g., 2-D or 3-D) controldevice (e.g., a touch screen or external input device such as a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), asignal capture/generation device (e.g., a speaker, camera, and/ormicrophone), and a network interface device, which may also beconfigured to communicate via the bus.

Embodiments of the computing system corresponding to a client device mayinclude a different configuration than an embodiment of the computingsystem corresponding to a server. For example, an embodimentcorresponding to a server may include a larger storage unit, morememory, and a faster processor but may lack the display driver, inputdevice, and dimensional control device. An embodiment corresponding to aclient device (e.g., a personal computer (PC)) may include a smallerstorage unit, less memory, and a more power efficient (and slower)processor than its server counterpart(s).

The storage unit includes a non-transitory computer-readable medium onwhich is stored instructions (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions mayalso reside, completely or at least partially, within the main memory orwithin the processor (e.g., within a processor's cache memory) duringexecution thereof by the computing system, the main memory and theprocessor also constituting non-transitory computer-readable media. Theinstructions may be transmitted or received over a network via thenetwork interface device.

While non-transitory computer-readable medium is shown in an exampleembodiment to be a single medium, the term “non-transitorycomputer-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store the instructions. The term“non-transitory computer-readable medium” shall also be taken to includeany medium that is capable of storing instructions for execution by thecomputing system and that cause the computing system to perform, forexample, one or more of the methodologies disclosed herein.

Portions of the system 100 of FIG. 1 may be located proximate to oneanother, while other portions may be located remote from some of theportions. For example, the image quality engine 104 may be located onthe premises for the office of the treating DVM, while the segmentationartificial intelligence engine(s) 106, the classification artificialintelligence(s) 108, and the output decision engine 110 may be locatedremote from the office of the DVM (e.g., within the “cloud”). Moreover,the treatment/recommendation graphical user interface (GUI) 112 may beresident in the office of the DVM as well as in the office of, forexample, the assignee of the present disclosure. In otherimplementations, each of the image quality engine 104, the segmentationartificial intelligence engine(s) 106 and the classification artificialintelligence engine(s) 108 may be located within the premises of theoffice of the DVM or may be located remote from the office of the DVM(e.g., resident within the cloud, resident within the offices of, forexample, the assignee hereof, etc.). These and other variants would bereadily apparent to one of ordinary skill given the contents of thepresent disclosure.

In some implementations, the DVM will install an application on anexemplary computing system located within, for example, the DVM's placeof business. This exemplary computing system may access a remotecomputing system (e.g., a computing system resident in the cloud) thatimplements some or all of the exemplary functionality disclosed herein.For example, the DVM may capture radiographic images of a subject animaland store these radiographic images on a local computer. Theseradiographic images may be transmitted over a network (e.g., theInternet) to the remote computing system (e.g., resident within thecloud). If these radiographic image(s) do not already contain metadatathat indicates basic criteria such as, for example, (a) species; (b)breed; (c) body positioning; and/or (d) image type, computer codelocated on the remote computing system may employ machine-learningalgorithms (such as those described herein), in order to determine suchbasic criteria.

The system 100 of FIG. 1 is directed towards the creation of a solutionto improve the quality of patient care for animals, improve access tocare for animal-owners, and/or enhance the capabilities and assist thetreating DVM. Specifically, the system 100 of FIG. 1 described hereinapplies machine learning to, for example, radiographic images (e.g.,digital imaging and communications in medicine (DICOM) images) in orderto assist in the identification and classification of various maladiesincluding, inter alia, bone fractures, organ abnormalities, chronicconditions, and/or identification/classification of other usefulinformation typically performed using, for example, biological imagingtechniques.

Biological data storage module 102 may receive biological data from adevice which captures the biological data. For example, in the contextof two-dimensional radiographic imaging (e.g., X-rays), the biologicaldata may be captured by the radiographic imaging machine and transmittedto/received by the biological data storage module 102. The biologicaldata storage module 102 may store other types of biological data inaddition to, or alternatively from, the aforementioned two-dimensionalradiographic imaging data. For example, three-dimensional imaging datamay be received from other types of imaging apparatus, including forexample, imaging data obtained from computed tomography (CT), magneticresonance imaging (MM), positron emission tomography (PET),single-photon emission computed tomography (SPECT), ultrasound(sonography) and/or other biological imaging techniques. The biologicaldata stored within biological data storage module 102 may also includenon-imaging data. For example, vital signs such as pulse, temperature,respiratory rate and the like may be stored within the biological datastorage module 102. Other types of biological data may be stored aswell, such as audio signals obtained via, for example, a stethoscope(e.g., auscultation of heart sound) and/or storage of blood, fecal,and/or other bodily fluid test results. These other biological data maybe used in addition to, or alternatively from, the biological imagingdata described herein to give a more holistic view of the health of ananimal.

In some implementations, the system 100 may include an image qualityengine 104 that may be governed by one or more quality controlparameters 116. One purpose of the image quality engine 104 may be tooquickly notify personnel that the biological data captured may be ofinsufficient quality in order to properly classify conditions associatedwith the animal. For example, in the context of captured imaging data,the image quality engine 104 may indicate that a part of the anatomy ofthe animal has been missed or may indicate that portions of the anatomyimportant for classification has only partially been captured. The imagequality engine 104 may also indicate that the images captured of theanimal have been improperly captured (e.g., the animal has not beenproperly positioned and/or the animal may have moved during imagecapture). The image quality engine 104 may also indicate that theimaging data may have been captured using less than optimal imagecapture settings. For example, the image quality engine 104 may detectwhen a captured image (or portions thereof) has been over-exposed and/orunder-exposed, which may be dependent upon, for example, the types ofdata intended to be captured. For example, a radiographic image that isadequately exposed for soft tissue structures may often be underexposedfor bony details, etc.

In some implementations, it may be desirable to implement the imagequality engine 104 at the premises of, for example, the DVM in order tofacilitate the speed of the indication. For example, capturingradiological images of an animal is oftentimes a significant stressor tothe animal undergoing radiological imaging. Accordingly, it may bedesirable to minimize the time and/or number of instances in which theanimal undergoes radiological imaging. Therefore, the use of the imagequality engine 104 may provide the technician with near instantfeedback, as well as instruction on how to better capture the image,thereby enabling the ability for the technician to quickly correct anyissues associated with the captured radiological images therebyobviating the need to have the animal undergo multiple radiologicalimage capture sessions. The image quality engine 104 may also applytransformations (e.g., rotations, translations, etc.) and normalizedfiltering to the received images in order to aid in segmentation andclassification as set forth below. Additional discussion of the imagequality engine 104 will be described subsequently herein with respect toFIG. 2A.

The system 100 may also include one or more segmentation artificialintelligence engine(s) 106. The one or more segmentation artificialintelligence engine(s) 106 may be trained using one or more segmentationtraining set(s) 112. For example, one segmentation artificialintelligence engine 106 may be configured to separate out the lungstructures of the animal, another segmentation artificial intelligenceengine 106 may be configured to separate out the heart structure of theanimal, while yet another segmentation artificial intelligence engine106 may be configured to separate out various skeletal structures of theanimal (e.g., the hips, the vertebra, knee joints, ankle joints, etc.).Each of these segmentation artificial intelligence engines 106 mayfurther be characterized by one or more individual segmentation trainingset(s) 112. For example, segmentation training set #1 112 a may beutilized with a first segmentation artificial intelligence engine 106,while another segmentation training set 112 n may be utilized for asecond segmentation artificial intelligence engine 106. In someinstances, a given segmentation artificial intelligence engine 106 maybe trained using two (or more) segmentation training sets 112. Forexample, for a vertebral heart score classification, a givensegmentation artificial intelligence engine 106 may require a firstsegmentation training set 112 for the heart of an animal and a secondsegmentation training set 112 for the vertebra (or portions thereof) ofthe animal. These and other variations would be readily apparent to oneof ordinary skill given the contents of the present disclosure.

The segmentation training set(s) 112 may include previously segmentedradiological images. These previously segmented radiological images mayhave been previously determined to be accurate by, for example, trainedveterinary specialists. For example, when classifying hip dysplasia, theanatomical structure around the pelvis of the animal may be determinedto be important. As but another non-limiting example, the lungstructures of the animal may be determined to be of importance for otherclassifications such as pleural effusion, pneumothorax conditions,pulmonary edemas and masses, etc. The previously segmented radiologicalimages may also be characterized dependent upon species or even thebreed of the animal. For example, a given segmentation training set 112may be associated with canine species, while another segmentationtraining set 112 may be associated with feline species. As but anothernon-limiting example, a given segmentation training set 112 may beassociated with smaller breed canines (e.g., a Chihuahua), while anothersegmentation training set 112 may be associated with larger breedcanines (e.g., a Great Dane). The segmentation training set 112 may alsobe updated to include ongoing segmented radiological images in order toprovide for more robust segmentation. These and other variants would bereadily apparent to one of ordinary skill given the contents of thepresent disclosure.

The segmentation artificial intelligence engine(s) 106 may utilize adeep learning approach (e.g., using region-based convolutional neuralnetworks (R-CNN), single shot multi-box detector (SSD) approaches,and/or You Only Look Once (YOLO) approaches, etc.) and/or machinelearning approaches such as support vector machine (SVM) to perform theobject classification/segmentation. As discussed supra, one purpose ofthe segmentation artificial intelligence engine(s) 106 is to segregatevarious anatomical regions of interest for a particular classificationintelligence engine 108. For example, when classifying hip dysplasiaconditions in a canine, the segmentation artificial intelligence engine106 may segregate the pelvis and head of the femur from the remainingskeletal structure of the captured radiographic images. This segmentedimage may then be fed to the classification artificial intelligenceengine 108 associated with classifying hip dysplasia in canines. As butanother non-limiting example, when classifying pleural effusion in ananimal, the segmentation artificial intelligence engine 106 may separatethe lung structures of the animal from other portions of the capturedradiological image. The segmented artificial engine 106 may also utilizethe separated lung structures of the animal for the purposes ofclassifying a pneumothorax condition (e.g., a collapsed lung), pulmonaryedema, pulmonary masses, etc. Other portions of the anatomy may besegmented dependent upon the classifications being performed (e.g.,heart and spine for vertebral heart scale classification, various jointsfor ligament, patella or stifle effusion classifications, vertebralstructure for spondylosis, pelvis for urinary bladder calculi, etc.).These and other anatomic classifications would be readily apparent toone of ordinary skill given the contents of the present disclosure.

The segmentation artificial intelligence engine(s) 106 may decrease thecomplexity associated with classification of various conditions in ananimal. Contrast this approach with prior manually read radiologicalimages in which diagnoses may only be determined based on a holisticview of the entire radiological image. In other words, trained personnel(e.g., veterinary radiologists) would not make diagnoses based onsegmented radiological images, rather the entire radiological imagewould be required in order to make a diagnosis of a particular animal.One drawback of this prior approach is that, for example, veterinaryradiologists may be subject to pre-conceived biases. For example, ananimal may have been brought into a veterinary clinic due to an owner'sconcerns about the animal's hips. The veterinary radiologist may findthemselves focusing on the animal's pelvic structure and may missdiagnoses for other problems that could also be present in otheranatomical structures of the animal. Accordingly, herein lies onesalient advantage of the present disclosure, namely the ability for thesystem 100 to classify the entirety of animal without necessarily beingsubjected to pre-conceived biases that are inherent with human-madeclassifications/diagnoses.

Once the radiological images have been segmented by the segmentationartificial intelligence engine(s) 106, these segmented radiologicalimages may be fed to one or more classification artificial intelligenceengines 108. Each of the classification artificial intelligence engines108 may be trained using one or more classification training sets 114.For example, classification artificial intelligence engine #1 108 a maybe trained using classification training set #1 114 a, whileclassification artificial intelligence engine #2 may be trained withclassification training set #1 114 a and classification training set #2114 b. Moreover, while not shown in FIG. 1 other classificationartificial intelligence engines 108 may be trained using three (or more)classification training sets 114. Moreover, classification artificialintelligence engines 108 may be added subsequent to the establishment ofprior classification artificial intelligence engines 108. In suchinstances, one or more extant classification training sets 114 may beutilized to train the newly added classification artificial intelligenceengine 108. Embodiments of the classification artificial engine 108training procedure will be discussed subsequently herein with respect toFIG. 2B.

The classification artificial intelligence engine(s) 108 may take theform of a deep learning approach such as, for example, a DenseConvolution Network (DenseNet), or may even take a machine learningapproach such as, for example and without limitation, those networkarchitectures discussed elsewhere herein. In embodiments that utilizeDenseNet, the specific architectures utilized for each of theclassification artificial intelligence engines 108 may vary. Forexample, classification intelligence engine #1 108 a may utilize aDenseNet-201 architecture, classification intelligence engine #2 108 bmay utilize a DenseNet-264 architecture, classification intelligenceengine #N 108 n may utilize a DenseNet-121 or DenseNet-169 architecture.These and other variants would be readily apparent to one of ordinaryskill given the contents of the present disclosure. In someimplementations, the architecture for each of the classificationartificial intelligence engines 108 may all be the same (or similar)(e.g., DenseNet-201).

The output from each of the classification artificial intelligenceengines 108 may further include a confidence level associated with theirdetermined classification. For example, each of the classificationartificial intelligence engines 108 may output a classification that isindicative of whether the determined classification is confident thatthe condition is normal, the condition is likely to be normal, thecondition is likely to be abnormal, or the classification of thecondition is confident to be abnormal. In some variants, the levels ofconfidence may be classified under one of three different levels with aclassification of one being indicative of a low (or high) confidencethat the classification condition is normal (or abnormal), aclassification of three being indicative of a high (or low) confidencethat the classification condition is abnormal (or normal), and aclassification of two being indicative of a confidence level betweenlevels one and three. Moreover, the classification predictions with ahigh degree of confidence may be added to the classification trainingsets 114, while classification predictions with a lower degree ofconfidence may not be added to the classification training sets 114.Moreover, the number value associated the predicted confidence level(e.g., three or four as described previously herein) may be increased insome implementations. For example, the number value associated with thepredicted confidence level may vary between one and ten, between one andfifty, or any other suitable number of levels. These and other variantswould be readily apparent to one of ordinary skill given the contents ofthe present disclosure.

One or more of the classification artificial engine(s) 108 may alsoassist with the identification of species and/or breed of the animal. Insome implementations, this identification of species and/or breed forthe animal may be compared against metadata associated with the capturedimage. It has been determined by the assignee of the present applicationthat radiological image metadata incorrectly identifies the breed and/orspecies of the animal undergoing image capture. For example, themetadata associated with an image captured of a feline may indicate thatthe associated imaging data has been captured of a canine due to, forexample, technician error (e.g., canine may be the default setting andthe technician neglects to update the metadata prior to/during/afterimage capture). In such instances, a classification artificialintelligence engine 108 may determine the discrepancy and may notify,for example, the technician responsible for the entering of the metadataof the error and/or may otherwise correct the metadata contained withincaptured radiological images. As but another non-limiting example, themetadata associated with a captured image may indicate that the imagehas been captured a large breed canine, even though the image wascaptured of a small breed canine. Accordingly, a classificationartificial intelligence engine 108 may determine the discrepancy and maynotify, for example, the technician responsible for the entering of themetadata of the error and/or may otherwise correct the metadatacontained within captured radiological images. These and other variantswould be readily apparent to one of ordinary skill given the contents ofthe present disclosure.

The system 100 may also include an output decision engine 110 which maytake as input one or more outputs from the classification artificialintelligence engine(s) 108. For example, an output from classificationartificial intelligence engine #1 108 a may indicate an abnormalvertebral heart score which may be symptomatic of cardiomegaly or anenlarged heart for the animal. Another output from classificationartificial intelligence engine #2 108 b may indicate that the metadataassociated with the captured images has incorrectly identified thespecies or breed of the animal that underwent image capture.Accordingly, output decision engine 110 may discount the findings ofartificial intelligence engine #1 108 a as the parameters associatedwith the abnormal vertebral heart score may have been misapplied as theoutput of artificial intelligence engine #1 108 a may have beenoperating under the incorrect assumption that the metadata associatedwith the captured images was correct. The output decision engine 110 maythen re-assess the vertebral heart score given this updated metadata (ormay signal to the classification artificial intelligence engine #1 108 ato reassess given this updated metadata). In other words, the outputdecision engine 110 may assess the various outputs from theclassification artificial intelligence engines 108 to ensure that theirresults holistically “make sense.”

The system 100 may also include a classification graphical userinterface 112 that outputs the result of the output decision engine 110.The output of the classification graphical user interface 112 mayindicate the classification of a variety of differing conditions (e.g.,pulmonary edema, pulmonary mass(es), pleural effusion, pneumothorax, hipdysplasia, spondylosis, stifle effusion, urinary bladder calculi, etc.).See also FIGS. 3A-3F described subsequently herein. The output of theclassification graphical user interface 112 may indicate whether or notthe classification is determined to be normal or abnormal for each ofthe differing conditions and may also indicate a confidence level (e.g.,confident condition is normal or confident that the condition isabnormal or that the condition is likely normal or likely abnormal,etc.). The classification graphical user interface 112 may also output(or display) the radiological image and may further include a graphicaldisplay of the area of segmentation utilized for the classification of agiven condition. For example, when outputting the classification for hipdysplasia, the graphical display may highlight an area around the pelvicstructure for the animal. The classification graphical user interface112 may also display one or more differing radiological images capturedfor a given animal. The classification graphical user interface 112 maythen indicate which one(s) of these differing radiological imagescaptured were utilized for the classification of a given condition.Embodiments of the classification graphical user interface will bedescribed subsequently herein with respect to FIGS. 3A-3F.

The graphical user interface 112 may also display various treatmentrecommendations that are dependent upon the determination of the outputdecision engine 110. These various treatment recommendations may be madebased upon historical treatment outcomes for a given animal, a givenbreed of animal, and/or a given species of animal. For example,medication A may be recommended for a canine that has been classifiedwith an abnormal classified condition A and an abnormal classifiedcondition B, yet has a normal classified condition C. Medication B maybe recommended for a canine that has been classified with abnormalclassified conditions A, B, and C. No medication may be recommended fora canine that has been classified with abnormal condition A, and normalclassified conditions B and C. These and other variants would be readilyapparent to one of ordinary skill given the contents of the presentdisclosure. Specific implementation details for the system 100 will nowbe described in subsequent detail infra.

Exemplary Quality Control Methodologies—

Referring now to FIG. 2A, one exemplary quality control methodology forthe image quality engine 104 is shown and described in detail. At step202, the image quality engine 104 receives radiological images capturedusing the radiological equipment. The received radiological images maytake the form of any standard imaging format including, withoutlimitation, Analyze, Neuroimaging Informatics Technology Initiative(Nifti), Minc, and Digital Imaging and Communications in Medicine(Dicom). The images may be received directly from the radiologicalequipment used to capture the images or may even be received from astorage apparatus (e.g., a hard drive) which stores the images capturedby the radiological equipment. In some implementations, the receivedimages include both imaging data (e.g., pixel data) as well as metadata.For example, Dicom imaging data not only contains pixel data, but alsoincludes metadata which includes a description of the medical procedurewhich led to the formation of the image itself. Common metadataassociated with, for example, the Dicom imaging format may includeinformation that describes the procedure for capturing the images (e.g.,acquisition protocol and scanning parameters) as well as patientinformation (e.g., species, breed, age and the like).

At step 204, the received images have transformation operations and/orfiltering applied to them. For example, the transformation operationsmay include rotation operations to ensure consistency with the datacontained within, for example, the segmentation training set 112 and/orthe classification training set. As but one non-limiting example, theimage quality engine 104 may determine the positioning techniqueutilized for the image capture via, for example, metadata associatedwith the imaging data or based from machine learning or artificialintelligence approaches. For example, the image quality engine 104 maydetermine that the received radiological images constitute one of alateral medial (LM) view, a lateral projection of the body cavities forthe animal (e.g., abdomen, thorax), a dorsoventral (DV) view, aventrodorsal (VD) view, a craniocaudal view, a dorsal palmar (DP) view,or a palmar dorsal (PD) view. Upon determination of the positioningtechnique utilized for the image capture, the image may be reorientedthrough rotations and/or translations so as to be consistent with, forexample, the training set 112, 114 data. The received images may alsoundergo normalization techniques that alter the range of pixel intensityvalues so as to, again, maintain continuity between the received imagesand the training set 112, 114 data.

The image quality engine 104 may also determine whether essentialanatomy has been missed (step 206), whether the animal was moving duringimage capture (i.e., twisted image) (step 208), and other whetherradiological burn through has been detected (step 210). For example, atstep 206 the image quality engine 104 may determine that only a partialimage of the lung structure has been captured and therefore mayoptionally discard the image and transmit a notification of theidentified issue(s) at step 210. In some implementations, the image maynot be discarded and instead will not attempt to classify the missinganatomy using the classification artificial intelligence engine(s) 108.For example, the lung structures may have been captured inadequately,but the other structures of the animal may have been adequatelycaptured. A notification may be transmitted to the DVM or othertechnician responsible for image capture highlighting that the lungstructures have been inadequately captured and the DVM or othertechnician responsible for image capture may determine that analysis ofthe lung structures is not needed based on, for example, previousassessment of the animal. These and other variants would be readilyapparent to one of ordinary skill given the contents of the presentdisclosure.

As but another non-limiting example, the image quality engine 104 maydetect a twisted radiological image and therefore may optionally discardthe image and transmit a notification of the identified issue at step212. As yet another non-limiting example, the image quality engine 104may determine that improper energy settings have been applied to theanimal for the captured radiological image which may be indicative ofunder-exposure or over-exposure (e.g., whether more energy needs to beapplied to a larger animal or less energy needs to be applied to asmaller animal, etc.) and may optionally discard the image and transmita notification of the identified issue(s) at step 210.

Herein lies one salient advantage of use of the image quality engine104, namely, to indicate to the technician or DVM in near-real-time ofthe inadequacy of the radiological image capture. As discussed elsewhereherein, capturing radiological images of an animal can be stressful forthe animal and the near-real-time indication to the technician or DVMmay allow for the quick adjustment or re-capture of the radiologicalimage which may minimize the number of procedures and/or the time thatthe animal has to undergo this otherwise stressful event. In someimplementations, this notification may take on any number of formsincluding a light on a device (e.g., an LED), a communication (e.g., anemail or text message to a specified address), an audible indication ona device (e.g., a beep or some other specified tone or sound) etc.,which not only identifies that there is a quality control issue with thecaptured radiological image, but may also indicate what the identifiedissue is (e.g., missed anatomy and what specific portion of the anatomyhas been missed, animal movement during image capture, and/or thedetection of improper radiological image capture settings). Uponsuccessful recapture of a radiological image of sufficient quality, thecaptured radiological image is transmitted to the artificialintelligence segmentation engine(s) 106 at step 214. In someimplementations, the image quality engine 104 may also indicate to thetechnician or DVM successful image capture though a light on a device(e.g., an LED), a communication (e.g., an email or text message to aspecified address), an audible indication on a device (e.g., a beep orsome other specified tone or sound) etc.

Referring now to FIG. 2B, a logical flow diagram of one exemplary methodfor the training of a classification artificial intelligence engine 108is shown and described in detail. Specifically, once the segmentationartificial intelligence engine(s) 106 has transmitted the segmentedradiological images to the respective classification artificialintelligence(s) 108, a training set assistance procedure 200 isimplemented. The training set assistance procedure 200 first verifiesthe output of the classification by using a quality control group atstep 218. For example, a trained human may verify the output of theclassification (e.g., confident abnormal, likely abnormal, likelynormal, and confident normal). If the trained human disagrees with theoutput of the classification artificial intelligence engine(s) 108, thetrained human may update the classification (e.g., from likely abnormalto confident abnormal, as but one non-limiting example). Conversely, ifthe trained human agrees with the output of the classificationartificial intelligence engine 108, the outputted classification is notupdated. Regardless of whether or not the trained human agrees ordisagrees with the output of the classification artificial intelligenceengine 108, the results may then be passed along to, for example, atrained DVM or supervisor. The trained DVM or supervisor may then updatethe classification, if necessary, prior to forwarding on the results tothe clinician who captured the radiological image.

If a strong prediction (e.g., confident abnormal or confident normal)has been verified by the DVM or supervisor, the segmented radiologicalimage may be added to the classification training set 114 at step 230.In some implementations, the segmented radiological image may be addedto the classification training set 114 at step 230, regardless of theconfidence of the prediction. Regardless of whether or not theprediction (classification) from the classification artificialintelligence engine 108 is indicative of a strong classification, theresults of the quality control group are forwarded on to the requestingclinician at step 222, where the results of the classification areverified by the clinician. If the requesting clinician disagrees withthe classification at step 222, the requesting clinician's findings areforwarded back to the quality control group for re-review at step 224.In some implementations, the requesting clinician will forward theirfindings back to the quality control group whether or not they disagreewith the classification. Upon re-review, the quality control group(e.g., the trained human and/or the DVM or supervisor) will determinewhether or not they agree with the requesting clinician's findings atstep 226. In some implementations, the results of the re-review are notforwarded back to the requesting clinician. One potential reason for notforwarding back the clinician verification is to encourage cooperationfrom the requesting clinicians in the training set assistance procedure.

If the re-review of the clinician's disagreement with the classificationresults are verified by the quality control group, the dataset may beremoved (or otherwise updated) from the training set at step 228. Insome implementations, so long as the dataset was previously added to thetraining set as a result of a strong prediction classification, thedataset may be updated or removed. Once the classification resultsexceed a threshold value at step 232, the training set assistanceprocedure may be removed from the process at step 234. Upon removal ofthe training set assistance procedure, strong predictions output by theclassification artificial intelligence engine 108 may continue to beadded to the classification training set 114. If however, theclassification results do not exceed the threshold value at step 232,continued use of the quality control group and the training setassistance procedure 200 is utilized.

The training set assistance procedure 200 may be performed individuallyfor each of the classification artificial intelligence engines 108. Forexample, the classification results for classification artificialintelligence engine #1 108 a may exceed the classification threshold andtherefore the training set assistance procedure 200 for classificationartificial intelligence engine #1 108 a may be turned off. Conversely,the classification results for classification artificial intelligenceengine #2 108 b may not exceed the classification threshold andtherefore the training set assistance procedure 200 for classificationartificial intelligence engine #2 108 b may continue to be utilized.Accordingly, the exemplary method for the training of a classificationartificial intelligence engine 108 is considered a robust procedure thatwill encourage its widespread adoption throughout, for example, theveterinary community. Moreover, the classification results threshold maybe chosen dependent upon, for example, the desirability of thewidespread adoption of the system 100. In some implementations, theclassification results threshold may be set to 95%, although lower orhigher classification results may be chosen in accordance with, forexample, the goals established by the operator of the system 100.

Exemplary Classification Graphical User Interfaces—

Referring now to FIGS. 3A-3F, various classification graphical userinterface displays 112 are shown and described in detail. FIG. 3Aillustrates a graphical user interface display 112 that includes aplurality of columns. For example, column 302 may include the date/timeof image capture, column 304 may the name(s) of the animals owner,column 306 may include the name of the animal that underwent imaging,column 308 may include the patient's identification number, and column310 may include a summary of classified maladies. In someimplementations, one or more of the aforementioned columns may beremoved from the graphical user interface display 110. In otherimplementations, one or more additional columns may be added in additionto (or alternatively than) the columns shown in FIG. 3A. For example,instead of containing a single classification column 310, two or moreclassification columns may be displayed on the graphical user interfacedisplay 110. One classification column 310 may indicate abnormalclassification(s), while another classification column 310 may indicatenormal classification(s). These and other variants would be readilyapparent to one of ordinary skill given the contents of the presentdisclosure.

The classification column 310 may include a colored box along withinitials. The initials may be representative of an identifiedclassification, while the color may be indicative of the confidencelevel of the classification and may also be indicative of whether or notthe classification is normal or abnormal. For example, the red color maybe indicative of a confident abnormal classification, while the orangecolor may be indicative of a likely abnormal classification. Additionalcolors may be utilized for likely normal and confident normalclassifications. Other color schemes may be utilized in someimplementations. Additionally, while initials for variousclassifications are illustrated in FIG. 3A, in some implementation'sfull words or even numbers (or symbols) may be utilized in addition to,or alternatively than, the aforementioned initials.

FIG. 3B illustrates another exemplary graphical user interface display112. On the left-hand side of the display 112, column 312 is shown withfive (5) different images. Each of these images have been taken of thesame animal. In some implementations, more (six (6) or more) or less(four (4) or fewer) images may have been captured. The image highlightedin blue is the same as the larger image 314 in the center of thedisplay. In the right-hand column 316, various classifications aredisplayed along with an indication of whether the classification isnormal or abnormal. In addition to the classification of normal orabnormal, a confidence indicator is associated with the normal orabnormal rating. For example, in the classification for condition 318,coloring (e.g., red) is indicative of an abnormal classification. Nextto the abnormal classification may be a series of four columns with eachcolumn having a different height. As can be seen in condition 318, allfour columns are highlighted in red which is indicative of a confidentabnormal classification. If only two of the four columns were colored,this would be indicative of a likely abnormal classification. Forcondition 320, the coloring (e.g., green) is indicative of a normalclassification, while the number of green columns is indicative of theconfidence level of the classification. For example, four coloredcolumns are indicative of “strong” classification, while less coloredcolumns (e.g., two) would be indicative of a less confidentclassification, etc.

FIG. 3C illustrates yet another exemplary graphical user interfacedisplay 112. Similar to FIG. 3B, on the left-hand side of the display112, column 312 is shown with five (5) different images. Each of theseimages have been taken of the same animal. Similar to that shown in FIG.3B, the image 324 highlighted in blue is the same as the larger image314 in the center of the display. In the right-hand column, variousclassifications are displayed along with an indication of whether theclassification is normal or abnormal. As can be seen in condition 326(hip dysplasia), two of the four columns are highlighted in green whichis indicative of a likely normal classification. A segmentation outline328 is overlaid on the center image 314 which is indicative of thesegmented portion of the radiological image used for the condition 326classification. The condition 326 has been selected as indicated by thegreen highlighting on the left-hand side of the condition. Similarly,the left-hand column 312 includes green highlighting 322. The greenhighlighting 322 indicates that this is the only image of the five (5)total shown used for the classification for hip dysplasia. Greenhighlighting is used for both the condition 326 as well as the image 322as this condition is classified as normal. However, if this conditionwere considered abnormal, the highlighting would be red. While color andpositioning of the various aspects within the graphical user interfacedisplay 112 of FIG. 3C is shown, it would be recognized by one ofordinary skill given the contents of the present disclosure that thecoloring and/or positioning scheme illustrated could be readily modifiedin alternative variants.

FIG. 3D illustrates yet another exemplary graphical user interfacedisplay 112. The display 112 shown in FIG. 3D is similar to that shownin FIG. 3C; however, in the embodiment of FIG. 3D, the condition 326selected is vertebral heart score. As can be seen in the left-handcolumn 312 two (2) (of the six (6) total) images contain greenhighlighting 322. The reasoning that two (2) of these images includegreen highlighting is because these two (2) images were utilized inclassifying the vertebral heart score for the animal. Additionally, thesegmentation outline 328 is now around the heart of the animalindicating that this portion of the image was segmented out prior tobeing classified.

FIG. 3E illustrates yet another exemplary graphical user interfacedisplay 112. In this display 112, the condition 326 pleural effusion hasbeen selected. The center image 314 is the image 330 that is currentlyselected. However, because the condition 326 classification is based onan image 332 that has not been selected, there is no segmentationoutline located within the center image 314 of FIG. 3E. However, if theimage 332 was selected, there would be a segmentation outline (here thelung structure) that would be indicative of the segmented portion of theradiological image.

FIG. 3F illustrates yet another exemplary graphical user interfacedisplay 112. In this display 112, the image 334 highlighted in blue isthe same as the image 314 in the center of the display. The selectedimage 334 also identifies what classifications 336 have been determinedbased on the selected image 334. In this example display 112, only asingle classification (pneumothorax) has been determined based on thisimage, although it would be readily appreciated that a given image maybe utilized for two (or more) classifications. These and other variantswould be readily apparent to one of ordinary skill given the contents ofthe present disclosure.

FIG. 3G illustrates yet another exemplary graphical user interfacedisplay 112. In this display 112, a summary of the findings of theclassification artificial intelligence engine(s) is displayed to theuser. The display 112 includes the name of the patient as well as thename of the treating veterinary facility. The display also includes asummary of the various normal/abnormal findings made using the system100. These normal/abnormal findings may be broken down by type (e.g.,thorax, bone, abdomen, etc.). Additionally, representative images may beincluded which were used in these normal/abnormal classifications. Thedisplay 112 may also include recommended treatment options for thepatient in some implementations. These recommended treatment options maybe updated over time as various outcomes for the recommended treatmentoptions become known. In other words, these recommended treatmentoptions may be updated to include information as to treatment success asa function of various other identified normal/abnormal criteria. While aparticular display 112 is shown, it would be readily apparent to one ofordinary skill that the display 112 would vary from animal-to-animal.

It will be recognized that while certain aspects of the presentdisclosure are described in terms of specific design examples, thesedescriptions are only illustrative of the broader methods of thedisclosure and may be modified as required by the particular design.Certain steps may be rendered unnecessary or optional under certaincircumstances. Additionally, certain steps or functionality may be addedto the disclosed embodiments, or the order of performance of two or moresteps permuted. All such variations are considered to be encompassedwithin the present disclosure described and claimed herein.

While the above detailed description has shown, described, and pointedout novel features of the present disclosure as applied to variousembodiments, it will be understood that various omissions,substitutions, and changes in the form and details of the device orprocess illustrated may be made by those skilled in the art withoutdeparting from the principles of the present disclosure. The foregoingdescription is of the best mode presently contemplated of carrying outthe present disclosure. This description is in no way meant to belimiting, but rather should be taken as illustrative of the generalprinciples of the present disclosure. The scope of the presentdisclosure should be determined with reference to the claims.

What is claimed is:
 1. A method for identification of variousphysiological conditions of an animal using machine learning, the methodcomprising: receiving one or more radiographic images captured of theanimal; receiving non-imaging biological data for the animal; segmentingthe received one or more radiographic images captured of the animalusing one or more segmentation artificial intelligence engines to createa set of segmented radiographic images, each of the set of segmentedradiographic images corresponding to a specific anatomical area withinthe animal, the segmenting of the received one or more radiographicimages captured of the animal comprises segregating a given organ and/ora given skeletal structure from remaining portions of the received oneor more radiographic images captured of the animal; providing the set ofsegmented radiographic images to respective ones of a plurality ofclassification artificial intelligence engines; providing the receivednon-imaging biological data for the animal to one or more of theplurality of classification artificial intelligence engines; outputtingresults from the plurality of classification artificial intelligenceengines for the set of segmented radiographic images and the non-imagingbiological data to an output decision engine, the outputting of theresults from the plurality of classification artificial intelligenceengines comprises outputting either a normal condition or an abnormalcondition along with a confidence level associated with the normalcondition or the abnormal condition; providing recommended courses ofaction, using the output decision engine, based on the output resultsfrom the plurality of classification artificial intelligence engines;analyzing metadata associated with the received one or more radiographicimages captured of the animal; comparing the analyzed metadata with theoutput results in order to determine misapplied parameters associatedwith a subset of the plurality of classification artificial intelligenceengines; and selectively discarding the output results associated withthe misapplied parameters.
 2. The method of claim 1, wherein theproviding of the recommended courses of action comprises providingfurther diagnostics that may be needed to treat the animal.
 3. Themethod of claim 1, wherein the providing of the recommended courses ofaction comprises providing treatment recommendations for the animal. 4.The method of claim 3, wherein the providing of the treatmentrecommendations for the animal are based on using one or more ofhistorical treatment outcomes for the animal, historical treatmentoutcomes for a given breed of the animal, and/or historical treatmentoutcomes for a given species of the animal.
 5. A non-transitorycomputer-readable storage apparatus comprising a plurality offinstructions, that when executed by a processor apparatus, areconfigured to: receive one or more radiographic images captured of ananimal; receive non-imaging biological data for the animal; segment thereceived one or more radiographic images captured of the animal via useof one or more segmentation artificial intelligence engines to create aset of segmented radiographic images, each of the set of segmentedradiographic images corresponding to a specific anatomical area withinthe animal; provide the set of segmented radiographic images torespective ones of a plurality of classification artificial intelligenceengines; provide the received non-imaging biological data for the animalto one or more of the plurality of classification artificialintelligence engines; output results from the plurality ofclassification artificial intelligence engines for the set of segmentedradiographic images and the non-imaging biological data to an outputdecision engine; provide recommended courses of action, via use of theoutput decision engine, based on the output results from the pluralityof classification artificial intelligence engines; output therecommended courses of action from the output decision engine to agraphical user interface (GUI), the GUI including the one or moreradiographic images, a centralized radiographic image from the one ormore radiographic images, and a plurality of classifications; receive afirst selection for one of the plurality of classifications; andhighlight one or more of the one or more radiographic images that wereutilized in assessing the first selection of the plurality ofclassifications.
 6. The non-transitory computer-readable storageapparatus of claim 5, wherein the plurality of instructions, whenexecuted by the processor apparatus, are further configured to: causedisplay of a first segmentation outline within the centralizedradiographic image, the first segmentation outline representing a firstanatomical area of interest utilized in the assessment of the firstselection for the one of the plurality of classifications.
 7. Thenon-transitory computer-readable storage apparatus of claim 6, whereinthe plurality of instructions, when executed by the processor apparatus,are further configured to: analyze metadata associated with the receivedone or more radiographic images captured of the animal; compare theanalyzed metadata with the output results in order to determinemisapplied parameters associated with a subset of the plurality ofclassification artificial intelligence engines; and selectively discardthe output results associated with the misapplied parameters.
 8. Thenon-transitory computer-readable storage apparatus of claim 6, whereinthe provision of the recommended courses of action comprises provisionof one or more diagnostics that may be needed to treat the animal. 9.The non-transitory computer-readable storage apparatus of claim 8,wherein the provision of the recommended courses of action comprisesprovision of one or more treatment recommendations for the animal.